8 papers
Terminal-World: Scaling Terminal-Agent Environments via Agent Skills
Zihao Cheng, Hongru Wang, Zeming Liu +6
Terminal agents extend Large Language Models with the ability to execute tasks directly in command-line environments, but their progress is bottlenecked by the scarcity of high-qua…
DocOS: Towards Proactive Document-Guided Actions in GUI Agents
Jingjing Liu, Ziye Huang, Zihao Cheng +6
While Graphical User Interface (GUI) agents have shown promising performance in automated device interaction, they primarily depend on static parametric knowledge from pre-training…
MemEvolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience Distillation
Zihao Cheng, Zeming Liu, Yingyu Shan +7
While large language model--powered agents can self-evolve by accumulating experience or by dynamically creating new assets (i.e., tools or expert agents), existing frameworks typi…
TCM-Eval: An Expert-Level Dynamic and Extensible Benchmark for Traditional Chinese Medicine
Zihao Cheng, Yuheng Lu, Huaiqian Ye +10
Large Language Models (LLMs) have demonstrated remarkable capabilities in modern medicine, yet their application in Traditional Chinese Medicine (TCM) remains severely limited by t…
Learn More, Forget Less: A Gradient-Aware Data Selection Approach for LLM
Yibai Liu, Shihang Wang, Zeming Liu +5
Despite large language models (LLMs) have achieved impressive achievements across numerous tasks, supervised fine-tuning (SFT) remains essential for adapting these models to specia…
RepoDebug: Repository-Level Multi-Task and Multi-Language Debugging Evaluation of Large Language Models
Jingjing Liu, Zeming Liu, Zihao Cheng +7
Large Language Models (LLMs) have exhibited significant proficiency in code debugging, especially in automatic program repair, which may substantially reduce the time consumption o…